Adaptive mean shift-based image segmentation using multiple instance learning
Bibliographic record
Abstract
Mean shift clustering tends to generate accurate segmentations of color images, but choosing the scale parameters remains a difficult problem which has a strong impact on its performance. We present an adaptive image segmentation framework that achieves a task-dependent top-down adaption of the scale parameters. The proposed method can be used under the context of a relevance feedback-based content-based image retrieval system. Standard mean shift clustering is used to generate an initial segmentation of the images in the database. After processing a query, the user gives the usual relevance feedback by labeling each of the images in the corresponding retrieval set as positive or negative, based on whether or not it contains a particular object of interest. In our approach, this feedback obtained as a by-product of user interaction with the system is then used in conjunction with multiple instance learning to induce a mapping from the object of interest to the scale parameters. The initial segmentation of the object of interest in each of the positive images in the database is then revised. This is done offline and is completely transparent to the user. Preliminary results indicate that the proposed method is capable of learning more informed segmentation parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".